Picture/graphics classification system and method

a classification system and graphic technology, applied in the field of image processing, can solve the problems of affecting the accuracy of image classification, so as to achieve less error, predict a confidence level, and reduce the effect of error

Inactive Publication Date: 2006-01-03
XEROX CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The present invention is a system that can classify images as either natural pictures or synthetic graphics with less error than previous classifiers. It uses new features and combinations of features to make this classification. Additionally, the system can predict a confidence level for picture and graphics image classification. The system also blends image processing functions with the classification to produce a more desirable output image. These technical effects make the system more accurate and efficient in identifying and classifying images.

Problems solved by technology

The patent text discusses the problem of accurately classifying images between natural pictures and synthetic graphics, which can be difficult due to the similarities in their appearance. Existing methods using binary classification can result in incorrect decisions, leading to objectionable image artifacts. The technical problem addressed by the invention is to provide new and improved methods for classifying images that overcome this issue and others.

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Embodiment Construction

[0034]Spatial gray-level dependence (SGLD) techniques for image analysis are well known. SGLD feature extraction creates a two-dimensional histogram that measures first and second-order statistics of an image. These features are captured in SGLD matrices. This was originally proposed for texture analysis of multi-level images. Additionally, since texture features distinguish natural pictures from synthetic graphics, SGLD techniques can be applied to picture / graphics classification of images. A picture / graphics classifier can be created with algorithms that analyze the texture features captured in SGLD matrices. Using the SGLD texture features, the classifier works to determine whether a scanned image is a natural picture or synthetic graphics. Furthermore, in color images, the luminance component typically contains enough information to determine the origin of the image. Therefore, an SGLD matrix that captures the luminance component of an image and a picture / graphics classifier usi...

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Abstract

A method and system for image processing, in conjunction with classification of images between natural pictures and synthetic graphics, using SGLD texture (e.g., variance, bias, skewness, and fitness), color discreteness (e.g., R—L, R—U, and R—V normalized histograms), or edge features (e.g., pixels per detected edge, horizontal edges, and vertical edges) is provided. In another embodiment, a picture/graphics classifier using combinations of SGLD texture, color discreteness, and edge features is provided. In still another embodiment, a “soft” image classifier using combinations of two (2) or more SGLD texture, color discreteness, and edge features is provided. The “soft” classifier uses image features to classify areas of an input image in picture, graphics, or fuzzy classes.

Description

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Claims

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Application Information

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Owner XEROX CORP
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